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- # COCO 2017 dataset http://cocodataset.org - first 128 training images
- # Train command: python train.py --data coco128.yaml
- # Default dataset location is next to YOLOv5:
- # /parent
- # /datasets/coco128
- # /yolov5
-
-
- # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
- path: ../datasets/coco128 # dataset root dir
- train: images/train2017 # train images (relative to 'path') 128 images
- val: images/train2017 # val images (relative to 'path') 128 images
- test: # test images (optional)
-
- # Classes
- nc: 80 # number of classes
- names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
- 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
- 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
- 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
- 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
- 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
- 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
- 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
- 'hair drier', 'toothbrush' ] # class names
-
-
- # Download script/URL (optional)
- download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
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